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Computing the Jacobian in spatial models: an applied survey


  • Bivand, Roger

    () (Dept. of Economics, Norwegian School of Economics and Business Administration)


Despite attempts to get around the Jacobian in fitting spatial econometric models by using GMM and other approximations, it remains a central problem for maximum likelihood estimation. In principle, and for smaller data sets, the use of the eigenvalues of the spatial weights matrix provides a very rapid and satisfactory resolution. For somewhat larger problems, including those induced in spatial panel and dyadic (network) problems, solving the eigenproblem is not as attractive, and a number of alternatives have been proposed. This paper will survey chosen alternatives, and comment on their relative usefulness.

Suggested Citation

  • Bivand, Roger, 2010. "Computing the Jacobian in spatial models: an applied survey," Discussion Paper Series in Economics 20/2010, Norwegian School of Economics, Department of Economics.
  • Handle: RePEc:hhs:nhheco:2010_020

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    Cited by:

    1. Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.

    More about this item


    Spatial autoregression; Maximum likelihood estimation; Jacobian computation; Econometric software.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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